/usr/local/lib64/python3.6/site-packages/caffe2/python/layers
Edit: /usr/local/lib64/python3.6/site-packages/caffe2/python/layers/sparse_feature_hash.py (4618B)
## @package sparse_feature_hash
# Module caffe2.python.layers.sparse_feature_hash
from caffe2.python import schema, core
from caffe2.python.layers.layers import (
ModelLayer,
IdList,
IdScoreList,
)
from caffe2.python.layers.tags import (
Tags
)
import numpy as np
class SparseFeatureHash(ModelLayer):
def __init__(self, model, input_record, seed=0, modulo=None,
use_hashing=True, use_divide_mod=False, divisor=None, name='sparse_feature_hash', **kwargs):
super(SparseFeatureHash, self).__init__(model, name, input_record, **kwargs)
assert use_hashing + use_divide_mod < 2, "use_hashing and use_divide_mod cannot be set true at the same time."
if use_divide_mod:
assert divisor >= 1, 'Unexpected divisor: {}'.format(divisor)
self.divisor = self.create_param(param_name='divisor',
shape=[1],
initializer=('GivenTensorInt64Fill', {'values': np.array([divisor])}),
optimizer=model.NoOptim)
self.seed = seed
self.use_hashing = use_hashing
self.use_divide_mod = use_divide_mod
if schema.equal_schemas(input_record, IdList):
self.modulo = modulo or self.extract_hash_size(input_record.items.metadata)
metadata = schema.Metadata(
categorical_limit=self.modulo,
feature_specs=input_record.items.metadata.feature_specs if input_record.items.metadata else None,
expected_value=input_record.items.metadata.expected_value if input_record.items.metadata else None
)
with core.NameScope(name):
self.output_schema = schema.NewRecord(model.net, IdList)
self.output_schema.items.set_metadata(metadata)
elif schema.equal_schemas(input_record, IdScoreList):
self.modulo = modulo or self.extract_hash_size(input_record.keys.metadata)
metadata = schema.Metadata(
categorical_limit=self.modulo,
feature_specs=input_record.keys.metadata.feature_specs,
expected_value=input_record.keys.metadata.expected_value
)
with core.NameScope(name):
self.output_schema = schema.NewRecord(model.net, IdScoreList)
self.output_schema.keys.set_metadata(metadata)
else:
assert False, "Input type must be one of (IdList, IdScoreList)"
assert self.modulo >= 1, 'Unexpected modulo: {}'.format(self.modulo)
if input_record.lengths.metadata:
self.output_schema.lengths.set_metadata(input_record.lengths.metadata)
# operators in this layer do not have CUDA implementation yet.
# In addition, since the sparse feature keys that we are hashing are
# typically on CPU originally, it makes sense to have this layer on CPU.
self.tags.update([Tags.CPU_ONLY])
def extract_hash_size(self, metadata):
if metadata.feature_specs and metadata.feature_specs.desired_hash_size:
return metadata.feature_specs.desired_hash_size
elif metadata.categorical_limit is not None:
return metadata.categorical_limit
else:
assert False, "desired_hash_size or categorical_limit must be set"
def add_ops(self, net):
net.Copy(
self.input_record.lengths(),
self.output_schema.lengths()
)
if schema.equal_schemas(self.output_schema, IdList):
input_blob = self.input_record.items()
output_blob = self.output_schema.items()
elif schema.equal_schemas(self.output_schema, IdScoreList):
input_blob = self.input_record.keys()
output_blob = self.output_schema.keys()
net.Copy(
self.input_record.values(),
self.output_schema.values()
)
else:
raise NotImplementedError()
if self.use_hashing:
net.IndexHash(
input_blob, output_blob, seed=self.seed, modulo=self.modulo
)
else:
if self.use_divide_mod:
quotient = net.Div([input_blob, self.divisor], [net.NextScopedBlob('quotient')])
net.Mod(
quotient, output_blob, divisor=self.modulo, sign_follow_divisor=True
)
else:
net.Mod(
input_blob, output_blob, divisor=self.modulo, sign_follow_divisor=True
)